Stock movement prediction with sentiment analysis based on deep learning networks. (17th November 2020) - Shi, Yong; Zheng, Yuanchun; Guo, Kun; Ren, Xinyue
Abstract: With the development of Internet and big data, it is more convenient for investors to share opinions or have a discuss with others via the web, which creates massive unstructured data. These data reflect investors' emotions and their investment intentions, and it will further affect the movement of the stock market. Although researchers have been attempted to use sentiment information to predict the market, the sentiment features used are driven by outdated emotion extraction systems. In this article, we proposed a new sentiment analysis system with deep neural networks for stock comments and applied estimated sentiment information to the stock movement forecasting. The empirical results showed that our deep sentiment classification method achieved a 9% improvement over the logistic regression algorithm, and provided an accurate sentiment extractor for the next predicting step. In addition our new hybrid features that mix stock trading data and sentiment information achieved 1.25% improvement among 150 Chinese stocks in the testing dataset. For American stocks, the sentiment information would reduced the predicting results. We found that emotion features extracted from comments are indeed effective for stocks with a higher price to book value and a lower beta risk value in China.